from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Graph
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D,UpSampling2D
from keras.optimizers import SGD
from keras.utils import np_utils
import keras.backend as K
from keras.regularizers import *

def SRCNN(img_channels,img_rows,img_cols):
    model = Sequential()
    model.add(Convolution2D(64, 9, 9, b_constraint=0,activation='relu',
                            input_shape=(img_channels, img_rows, img_cols)))
    model.add(Ba)
    model.add(Convolution2D(32, 1, 1, activation='relu',b_constraint=0))
    model.add(Convolution2D(img_channels, 5, 5, b_constraint=0))
    model.summary()
    return model

def SRCNN_test(img_channels,img_rows,img_cols):
    model = Sequential()
    model.add(Convolution2D(64, 9, 9, border_mode='same',b_constraint=0,activation='relu',
                            input_shape=(img_channels, img_rows, img_cols)))
    model.add(Convolution2D(32, 1, 1, b_constraint=0,activation='relu'))
    model.add(Convolution2D(img_channels, 5, 5, border_mode='same',b_constraint=0))
    model.summary()
    return model